You know your data quality must be fixed, but convincing others isn’t always easy. After all they don’t have the problem of answering queries from account managers when someone’s name has been spelt wrong or that shipment is lost in the ether or the latest newsletter never arrived, and so on.
You know that you need to clean the data, but how are you going to convince management or the steering committee to part with some money, they see it as a nice to have or just not a problem.
But why is there apathy towards data quality in some organisations? Well, we thought we would map out the different types of data from a commercial viewpoint to see why this is the case.
Look at this diagram, we call it The Data Stack, you immediately see that Data Quality is at the bottom, but that’s just reading the diagram in correctly. Data Quality is the foundation for all the other types of data. An error at the data quality layer has a big impact on the layers above it.
We’ve posted articles about data quality ROI using simple email and direct mail campaigns. When you see how a missing email leads to lost opportunities, which leads to lost revenues, which leads to lost recurring revenues then fixing data quality problems becomes an insignificant cost and an urgent fix.
- Sales & Marketing ROI – These are the big numbers that directors love to see, what are our sales conversions, what are the returns on the direct selling team, which marketing campaigns are the best, etc.
- Business Intelligence – This is the inside commercial information that provides the bones to the above layer. Which products are selling, what are our up-sell and cross-sell conversions rates per product/service/account, how well is the market place engaging with us online?, etc.
- Data Intelligence – Do we know everything we need to know about our target markets, what is our level of segmentation, do we have all the required intelligence on existing and new accounts, etc.
- Data Quality – Is the information we have correct, does it have integrity, is it improving with time, have we stopped it from degrading, etc.
Each of the layers affects the layer next to it. Errors at the data quality layer will mean incorrect figures for management; incorrect data intelligence will affect the results at the business intelligence layer, and so on.
At a quick glance at The Data Stack, you can see by fixing the foundations of data quality, accurate and reliable business data is made possible.
We started by saying, in some organisations there is apathy towards data quality, probably through lack of understanding; and when we see that data quality is at the bottom of the stack, no wonder this perception occurs. (Management may not have the concept of The Data Stack, but the subconcious will rate it lower.) Once you look at the ROI and the affect of poor data quality, it couldn’t be more important.
So use The Data Stack as a tool to explain to others how this works and position data quality appropriately. In the coming weeks we will be looking at each layer in more detail.